A multi-spectral image is a collection of two or more monochrome images of the same scene. Multi-spectral images can be described in any one of a plurality of known spectral or color spaces. For example, one well-known multi-spectral image is an RGB color image. An RGB color image consists of a red, a green, and a blue component and, thus, the image is said to be described in RGB spectral space. Other spectral spaces (sometimes hereinafter referred as color space(s)) include the CIE (Commission Internationale de L'Eclairage) L*a*b*, CIE XYZ, CIE L*u*v*, CIE YUV, CMY (Cyan, Magenta, Yellow), CMYK (Cyan, Magenta, Yellow, blacK), CCIR (Comite Consultatif International des Radiocommunications) 601 YCbCr, YIQ, HIS, and HSV spectral spaces. See Television Engineering Handbook, Featuring HDTV Systems, Revised Edition by K. Blair Benson, revised by Jerry C. Whitaker (McGraw-Hill, 1992) for more information on color spaces.
Digital multi-spectral images, as well as all digital images, are represented by an array of pixels. Each pixel of a digital multi-spectral image is defined by numerical components that represent the color of the pixel. For example, if a digital multi-spectral image is described in RGB spectral space, each pixel of the image is defined by three numerical values representing the colors of red, green, and blue.
One of the most common ways of generating a digital multi-spectral image in RGB spectral space is via a color scanner that is in communication with a computer system. The color scanner typically acquires the digital image by scanning a source (e.g., a photograph) with sensors sensitive to three color wavelengths: red, green, and blue. Upon completion of the scan, a digital multi-spectral image of the source is generated in RGB spectral space from the acquired data and can be displayed on a computer monitor or other display device.
Because digital images, and multi-spectral digital images in particular, are described by large amounts of data (e.g., in RGB spectral space, each pixel is described by three numerical values) various compression techniques have been developed to compress the image data to provide for the efficient storage, access and transmission of the digital image.
On a general level, data compression entails the coding of original data into secondary data, from which, the original data can again be derived. Generally, the secondary or coded data, will be quantitatively less than the original data. Data compression falls into two general categories: lossy and lossless. In a lossy system, data is compressed with the knowledge and foresight that the reconstructed data will not be an identical replica of the original data, but only a close approximation. Conversely, a lossless system is one that produces an exact replica of the original data from the compressed data. One well-known compression technique is defined by the JPEG (Joint Photographic Experts Group) 30 standard. A more recent JPEG standard for the lossless and near-lossless compression of images is called JPEG-LS. For more information on JPEG-LS, see Information Technology--Lossless and Near-Lossless Compression of Continuous-Tone Still Images, ISO/IEC CD 14495:1997(E) March 1997. Other well-known compression standards also exist, such as JBIG and GIF.
Lossy compression of image data is generally acceptable because it is known that the human eye perceives small changes in color less accurately than small changes in brightness. Accordingly, small losses in digital image information that impacts color data caused by lossy compression and decompression are acceptable. Furthermore, display devices such as computer monitors and televisions are inherently lossy in that they cannot display all of the information contained in an image and therefore small losses of image information are difficult to perceive on such lossy devices. Moreover, lossy compression and decompression techniques offer superior compression ratios compared to lossless compression and decompression techniques.
However, other devices such as digital printers, and digital color printers in particular, are far less tolerant of lossy image information. To compound this problem, most digital color printers require image information in the CMYK spectral space. Thus, an image that is described in RGB spectral space must be transformed into CMYK spectral space before the digital color printer can print it.
In addition to scanning in the RGB color space and printing in the CMYK color space, image compression is often done in a third color space. Most JPEG images are compressed in the YCbCr color space. For MPEG-1 this is the only allowed color space. The luminance component (Y) is compressed with better quality than the color components Cb and Cr since the human visual system is more forgiving of loss in these color components. However, the transformation from RGB to YCbCr and back to RGB (or on to CMY) is inherently lossy.
Accordingly, given a lossy image compression and decompression technique and a lossy spectral space transformation(s), digital multi-spectral image information may be significantly lost from the time the image is first acquired to the time it is, for example, printed on a digital color printer. The result is often a color image which is lacking in quality as compared to the original image. Hence, a method for compressing multi-spectral digital images which does not suffer from this and other disadvantages is desired.